Machine Learning And Deep Learning - Fundamentals And Applications

Exam Preparation PDF

ABOUT THE COURSE:

In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. We will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. On completion of the course students will acquire the knowledge of applying Machine and Deep Learning techniques to solve various real-life problems.

INTENDED AUDIENCE: UG, PG and PhD students and industry professionals who want to work in Machine and Deep Learning.

PREREQUISITES: Knowledge of Linear Algebra, Probability and Random Process, PDE will be helpful.

INDUSTRY SUPPORT: This is a very important course for industry professionals.

Course Status :	Ongoing
Course Type :	Core
Duration :	12 weeks
Category :	Computer Science and Engineering Electrical, Electronics and Communications Engineering Communication and Signal Processing
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	24 Jul 2023
End Date :	13 Oct 2023
Enrollment Ends :	07 Aug 2023
Exam Registration Ends :	18 Aug 2023
Exam Date :	29 Oct 2023 IST

Course layout

Week 1: Introduction
Introduction to ML, Performance Measures, Bias-Variance Trade off, Linear Regression.
Week 2: Bayes Decision Theory
Bayes Decision Theory, Normal Density and Discriminant Function, Bayes Decision Theory - Binary Features, Bayesian Belief Network
Week 3: Parametric and Non- Parametric Density Estimation
Parametric and Non- Parametric Density Estimation – ML and Bayesian Estimation, Parzen Window and KNN
Week 4:Perceptron Criteria and Discriminative Models
Perceptron Criteria, Discriminative models, Support Vector Machines (SVM)
Week 5: Logistic Regression, Decision Trees and Hidden Markov Model
Logistic Regression, Decision trees, Hidden Markov Model (HMM)
Week 6: Ensemble methods
Ensemble methods: Ensemble strategies, boosting and bagging, Random Forest
Week 7: Dimensionality Problem
Dimensionality Problem, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
Week 8: Mixture Model and Clustering
Concept of mixture model, Gaussian mixture model, Expectation Maximization Algorithm, K- means clustering.
Week 9: Clustering
Fuzzy K-means clustering, Hierarchical Agglomerative Clustering, Mean-shift clustering.
Week 10: Neural Network
Neural network: Perceptron, multilayer network, backpropagation, RBF Neural Network, Applications
Week 11: Introduction to Deep Neural Networks
Introduction to Deep Learning, Convolutional Neural Networks (CNN),
Vanishing and Exploding Gradients in Deep Neural Networks, LeNet - 5, AlexNet, VGGNet, GoogleNet, and ResNet.
Week 12: Recent Trends in Deep Learning
Generative Adversarial Networks (GAN), Auto Encoders and Relation to PCA, Recurrent Neural Networks, U-Net, Applications and Case studies.

Books and references

1. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India) 2015.

2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.

3. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics),Springer, 2006.

4. M.K. Bhuyan, Computer Vision and Image Processing: Fundamentals and Applications, published by CRC press, USA, 2019.

5. S. O. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson Education (India), 2016.

6. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016

7. Michael A. Nielsen, Neural Networks and Deep Learning , Determination Press, 2015

8. Yoshua Bengio, Learning Deep Architectures for AI, now Publishers Inc., 2009

Exam Preparation PDF

Part : 1

   

Part : 2

   

Part : 3